Multi-component models are often used to model dynamical systems that can be conveniently represented as a system of interacting components. Examples include models of the Earth's biosphere, whole organisms, biological cells, the immune system, and anthropogenic systems such as agricultural systems, automobiles and economic systems. A dynamical system in this context is one whose state can change through time as a result of the behavior of mechanisms internal to the system, although this could be in response to changes in factors external to the system. In multi-component models, individual aspects of the modeled system are each represented using a dedicated model component. The model components are then interconnected to form a whole multi-component model. Designing the model components and the way these are to be interconnected can be a complex task, particularly when the systems being modeled are not fully understood. For example, this is often the case for natural systems in which scientists only partially understand their functioning.
Empirical data is typically used to parameterize and evaluate multi-component models, especially when the underlying mechanisms of the system being modeled are not fully understood. This can be a challenging process because relevant data may come from multiple sources, in various formats, in variable quantities, and with different access permissions.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known multi-component model-engineering systems.